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dc.contributor.authorBarrett, H.H.
dc.contributor.authorCaucci, L.
dc.date.accessioned2021-10-16T02:18:43Z
dc.date.available2021-10-16T02:18:43Z
dc.date.issued2021
dc.identifier.citationBarrett, H. H., & Caucci, L. (2021). Stochastic models for objects and images in oncology and virology: Application to PI3K-Akt-mTOR signaling and COVID-19 disease. Journal of Medical Imaging, 8(1).
dc.identifier.issn2329-4302
dc.identifier.doi10.1117/1.JMI.8.S1.S16001
dc.identifier.urihttp://hdl.handle.net/10150/662110
dc.description.abstractPurpose: The goal of this research is to develop innovative methods of acquiring simultaneous multidimensional molecular images of several different physiological random processes (PRPs) that might all be active in a particular disease such as COVID-19. Approach: Our study is part of an ongoing effort at the University of Arizona to derive biologically accurate yet mathematically tractable models of the objects of interest in molecular imaging and of the images they produce. In both cases, the models are fully stochastic, in the sense that they provide ways to estimate any estimable property of the object or image. The mathematical tool we use for images is the characteristic function, which can be calculated if the multivariate probability density function for the image data is known. For objects, which are functions of continuous variables rather than discrete pixels or voxels, the characteristic function becomes infinite dimensional, and we refer to it as the characteristic functional. Results: Several innovative mathematical results are derived, in particular for simultaneous imaging of multiple PRPs. Then the application of these methods to cancers that disrupt the mammalian target of rapamycin signaling pathway and to COVID-19 are discussed qualitatively. One reason for choosing these two problems is that they both involve lipid rafts. Conclusions: We found that it was necessary to employ a new algorithm for energy estimation to do simultaneous single-photon emission computerized tomography imaging of a large number of different tracers. With this caveat, however, we expect to be able to acquire and analyze an unprecedented amount of molecular imaging data for an individual COVID patient. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
dc.language.isoen
dc.publisherSPIE
dc.rightsCopyright © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectcharacteristic functional
dc.subjectcoronavirus
dc.subjectlipid rafts
dc.subjectmammalian target of rapamycin signaling
dc.subjectstochastic models
dc.titleStochastic models for objects and images in oncology and virology: Application to PI3K-Akt-mTOR signaling and COVID-19 disease
dc.typeArticle
dc.typetext
dc.contributor.departmentUniversity of Arizona, Wyant College of Optical Sciences
dc.contributor.departmentUniversity of Arizona, Department of Medical Imaging
dc.identifier.journalJournal of Medical Imaging
dc.description.noteOpen access article
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleJournal of Medical Imaging
refterms.dateFOA2021-10-16T02:18:43Z


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Copyright © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.
Except where otherwise noted, this item's license is described as Copyright © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.